Welcome back!!!
There are a few chapters,
Part 2 might take a long take, but it will be fun!!
So let’s open R-studio. Behold the visage that greets thine eye.
library() calls to start the ‘game’!# Load the tidyverse package (game)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
# Load the ggplot2 package (game)
library(ggplot2)
# Load the ggpmisc package (game)
library(ggpmisc)
## Warning: package 'ggpmisc' was built under R version 4.3.3
## Loading required package: ggpp
## Registered S3 methods overwritten by 'ggpp':
## method from
## heightDetails.titleGrob ggplot2
## widthDetails.titleGrob ggplot2
##
## Attaching package: 'ggpp'
##
## The following object is masked from 'package:ggplot2':
##
## annotate
##
## Registered S3 method overwritten by 'ggpmisc':
## method from
## as.character.polynomial polynom
# Load the gganimate package (game)
library(gganimate)
# Load the animation package (game)
library(animation)
# Load the animation package (game)
library(kableExtra)
##
## Attaching package: 'kableExtra'
##
## The following object is masked from 'package:dplyr':
##
## group_rows
Let me remind you about the goal of MLRS 101
GOAL: learn how to make some results (plots / stats) with R-studio to answer your PI's incessant desire for "Any updates?"
Soooooo, before we jump into real genomics data to answer their question, let’s start with some popular culture data (basketball) to make some figures and present some stats.
Before we jump into any kind of data, do you know who is this?